Modern three-dimensional (3D) medical images obtained from Computed Tomography (CT) and Magnetic Resonance (MR) scanners contain a wealth of data that is time consuming for physicians to examine and interpret. Various important tools have been introduced to aid in their analysis, such as Computer-Aided Detection (CAD) and Computer-Aided Diagnosis (CADx) and different visualization modalities. Such tools require preprocessing steps that can include segmentation, candidate detection, feature collection, and classification. Classification can be used both to automatically label candidates as detected lesions/areas of suspected disease, and further to classify detected lesions/areas of suspected disease into different types.
In CADx systems that attempt to detect possible abnormalities or disease areas, segmentation, candidate detection, feature collection, and classification steps are commonly used. In segmentation, portions of the image that cannot contain the type of abnormality of interest are eliminated. For example, in colon polyp detection, areas outside the colon would not be considered. In lung nodule detection, areas outside the lungs would be eliminated. This step saves later processing time, since the outside areas do not need consideration, and also eliminates possible sources of false positives. In addition, many CADx systems rely on segmentation to generate a precise boundary between the object of interest and background in order to detect possible candidates or generate features for the candidates.
During the candidate detection stage, algorithms attempt to detect all or most of the possible disease areas. In order to have a high sensitivity, many false positives may also be detected at this stage. Later stages of the algorithm will attempt to remove the false positives while retaining the true positives. For example, in colon polyp detection, the candidate detector will attempt to find all the true polyps, but may also find normal folds of the colon, residual stool, and the ileocecal valve. In lung nodule detection, the candidate detector should find true nodules, but may also find vessel bifurcations, fissural and bronchial thickenings, and scars.
During the feature collection stage, algorithms compute features in and around each candidate that will be used to distinguish true from false positives. For example, in colon polyp detection, collected features may include intensity values and distributions. Similar features may be used for lung nodule detection. The classification stage analyzes the features collected in the previous step in an attempt to remove false positives while still retaining the true positives.
In current CAD methods, false positives that are created by the initial stages of an algorithm may be eliminated in later stages by collecting and analyzing specific features of each polyp candidate. In order to collect these features, some notion of the space occupied by the candidate must be utilized.
A point location alone, such as the detection point, will only permit a limited number of features to be collected. Some kind of estimate of the volume occupied by a colon polyp or lung nodule must exist to properly collect additional features. Typically, a large number of candidates are detected in the early phases of detection. Any feature collection method must operate quickly to process these candidates.
There is a need for a method to accurately identify and characterize potential candidates in an image in order to provide a proper diagnosis of a patient. This process requires recognition of the candidates and the ability to quickly and efficiently eliminate false positives while retaining true positives.